ViralCC retrieves complete viral genomes and virus-host pairs from metagenomic Hi-C data

Sequencing-based analysis of microbiomes

Microbiomes occupy a range of niches and, in addition to having diverse compositions, they have varied functional roles that have an impact on agriculture, environmental sciences, and human health and disease. The study of microbiomes has been facilitated by recent technological and analytical advances, such as cheaper and higher-throughput DNA and RNA sequencing, improved long-read sequencing and innovative computational analysis methods. These advances are providing a deeper understanding of microbiomes at the genomic, transcriptional and translational level, generating insights into their function and composition at resolutions beyond the species level.

Adaptive strategies and ecological roles of phages in habitats under physicochemical stress

Bacteriophages (phages) play a vital role in ecosystem functions by influencing the composition, genetic exchange, metabolism, and environmental adaptation of microbial communities. With recent advances in sequencing technologies and bioinformatics, our understanding of the ecology and evolution of phages in stressful environments has substantially expanded. Here, we review the impact of physicochemical environmental stress on the physiological state and community dynamics of phages, the adaptive strategies that phages employ to cope with environmental stress, and the ecological effects of phage-host interactions in stressful environments. Specifically, we highlight the contributions of phages to the adaptive evolution and functioning of microbiomes and suggest that phages and their hosts can maintain a mutualistic relationship in response to environmental stress. In addition, we discuss the ecological consequences caused by phages in stressful environments, encompassing biogeochemical cycling. Overall, this review advances an understanding of phage ecology in stressful environments, which could inform phage-based strategies to improve microbiome performance and ecosystem resilience and resistance in natural and engineering systems.

VirGrapher: a graph-based viral identifier for long sequences from metagenomes

Viruses are the most abundant biological entities on earth and are important components of microbial communities. A metagenome contains all microorganisms from an environmental sample. Correctly identifying viruses from these mixed sequences is critical in viral analyses. It is common to identify long viral sequences, which has already been passed thought pipelines of assembly and binning. Existing deep learning-based methods divide these long sequences into short subsequences and identify them separately. This makes the relationships between them be omitted, leading to poor performance on identifying long viral sequences. In this paper, VirGrapher is proposed to improve the identification performance of long viral sequences by constructing relationships among short subsequences from long ones. VirGrapher see a long sequence as a graph and uses a Graph Convolutional Network (GCN) model to learn multilayer connections between nodes from sequences after a GCN-based node embedding model. VirGrapher achieves a better AUC value and accuracy on validation set, which is better than three benchmark methods.

MetaCC allows scalable and integrative analyses of both long-read and short-read metagenomic Hi-C data

Metagenomic Hi-C (metaHi-C) can identify contig-to-contig relationships with respect to their proximity within the same physical cell. Shotgun libraries in metaHi-C experiments can be constructed by next-generation sequencing (short-read metaHi-C) or more recent third-generation sequencing (long-read metaHi-C). However, all existing metaHi-C analysis methods are developed and benchmarked on short-read metaHi-C datasets and there exists much room for improvement in terms of more scalable and stable analyses, especially for long-read metaHi-C data. Here we report MetaCC, an efficient and integrative framework for analyzing both short-read and long-read metaHi-C datasets. MetaCC outperforms existing methods on normalization and binning. In particular, the MetaCC normalization module, named NormCC, is more than 3000 times faster than the current state-of-the-art method HiCzin on a complex wastewater dataset. When applied to one sheep gut long-read metaHi-C dataset, MetaCC binning module can retrieve 709 high-quality genomes with the largest species diversity using one single sample, including an expansion of five uncultured members from the order Erysipelotrichales, and is the only binner that can recover the genome of one important species Bacteroides vulgatus. Further plasmid analyses reveal that MetaCC binning is able to capture multi-copy plasmids.